Artificial intelligence in cancer imaging: Clinical challenges and applications.

Judgement, as one of the core tenets of medicine, relies upon the integration of multilayered data with nuanced decision making. Cancer offers a unique context for medical decisions given not only its variegated forms with evolution of disease but also the need to take into account the individual condition of patients, their ability to receive treatment, and their responses to treatment. Challenges remain in the accurate detection, characterization, and monitoring of cancers despite improved technologies. Radiographic assessment of disease most commonly relies upon visual evaluations, the interpretations of which may be augmented by advanced computational analyses. In particular, artificial intelligence (AI) promises to make great strides in the qualitative interpretation of cancer imaging by expert clinicians, including volumetric delineation of tumors over time, extrapolation of the tumor genotype and biological course from its radiographic phenotype, prediction of clinical outcome, and assessment of the impact of disease and treatment on adjacent organs. AI may automate processes in the initial interpretation of images and shift the clinical workflow of radiographic detection, management decisions on whether or not to administer an intervention, and subsequent observation to a yet to be envisioned paradigm. Here, the authors review the current state of AI as applied to medical imaging of cancer and describe advances in 4 tumor types (lung, brain, breast, and prostate) to illustrate how common clinical problems are being addressed. Although most studies evaluating AI applications in oncology to date have not been vigorously validated for reproducibility and generalizability, the results do highlight increasingly concerted efforts in pushing AI technology to clinical use and to impact future directions in cancer care.

Applying polygenic risk score methods to pharmacogenomics GWAS: challenges and opportunities.

Polygenic risk scores (PRSs) have emerged as promising tools for the prediction of human diseases and complex traits in disease genome-wide association studies (GWAS). Applying PRSs to pharmacogenomics (PGx) studies has begun to show great potential for improving patient stratification and drug response prediction. However, there are unique challenges that arise when applying PRSs to PGx GWAS beyond those typically encountered in disease GWAS (e.g. Eurocentric or trans-ethnic bias). These challenges include: (i) the lack of knowledge about whether PGx or disease GWAS/variants should be used in the base cohort (BC); (ii) the small sample sizes in PGx GWAS with corresponding low power and (iii) the more complex PRS statistical modeling required for handling both prognostic and predictive effects simultaneously. To gain insights in this landscape about the general trends, challenges and possible solutions, we first conduct a systematic review of both PRS applications and PRS method development in PGx GWAS. To further address the challenges, we propose (i) a novel PRS application strategy by leveraging both PGx and disease GWAS summary statistics in the BC for PRS construction and (ii) a new Bayesian method (PRS-PGx-Bayesx) to reduce Eurocentric or cross-population PRS prediction bias. Extensive simulations are conducted to demonstrate their advantages over existing PRS methods applied in PGx GWAS. Our systematic review and methodology research work not only highlights current gaps and key considerations while applying PRS methods to PGx GWAS, but also provides possible solutions for better PGx PRS applications and future research.

Opportunities and challenges for ChatGPT and large language models in biomedicine and health.

ChatGPT has drawn considerable attention from both the general public and domain experts with its remarkable text generation capabilities. This has subsequently led to the emergence of diverse applications in the field of biomedicine and health. In this work, we examine the diverse applications of large language models (LLMs), such as ChatGPT, in biomedicine and health. Specifically, we explore the areas of biomedical information retrieval, question answering, medical text summarization, information extraction and medical education and investigate whether LLMs possess the transformative power to revolutionize these tasks or whether the distinct complexities of biomedical domain presents unique challenges. Following an extensive literature survey, we find that significant advances have been made in the field of text generation tasks, surpassing the previous state-of-the-art methods. For other applications, the advances have been modest. Overall, LLMs have not yet revolutionized biomedicine, but recent rapid progress indicates that such methods hold great potential to provide valuable means for accelerating discovery and improving health. We also find that the use of LLMs, like ChatGPT, in the fields of biomedicine and health entails various risks and challenges, including fabricated information in its generated responses, as well as legal and privacy concerns associated with sensitive patient data. We believe this survey can provide a comprehensive and timely overview to biomedical researchers and healthcare practitioners on the opportunities and challenges associated with using ChatGPT and other LLMs for transforming biomedicine and health.

Neurological manifestations of Flaviviridae, Togaviridae, and Peribunyaviridae as vector-borne viruses.

Vector-borne viruses pose a significant health problem worldwide, as they are transmitted to humans through the bite of infected arthropods such as mosquitoes and ticks. In recent years, emerging and re-emerging vector-borne diseases have gained attention as they can cause a wide spectrum of neurological manifestations. The neurological manifestations of vector-borne viruses encompass a board spectrum of clinical manifestations, ranging from mild and self-limiting symptoms to severe and life-threatening conditions. Common neurological complications include viral encephalitis, acute flaccid paralysis, aseptic meningitis, and various neuromuscular disorders. The specific viruses responsible for these neurological sequelae vary by geographic region and include Orthoflavivirus nilense, Zika virus, dengue virus, chikungunya virus, Japanese encephalitis virus, and tick-borne encephalitis virus. This review focuses on the pathogenesis of these neurologic complications and highlights the mechanisms by which vector-borne viruses invade the central nervous system and trigger neuroinflammatory responses. Diagnostic challenges and strategies for early detection of neurological manifestations are discussed, emphasising the importance of clinical suspicion and advanced laboratory testing.

Virus-like particles as powerful vaccination strategy against human viruses.

Nowadays, viruses are not only seen as causative agents of viral infectious diseases but also as valuable research materials for various biomedical purposes, including recombinant protein production. When expressed in living or cell-free expression systems, viral structural proteins self-assemble into virus-like particles (VLPs). Mimicking the native form and size of viruses and lacking the genetic material, VLPs are safe and highly immunogenic and thus can be exploited to develop antiviral vaccines. Some vaccines based on VLPs against various infectious pathogens have already been licenced for human use and are available in the commercial market, the latest of which is a VLP-based vaccine to protect against the novel Coronavirus. Despite the success and popularity of VLP subunit vaccines, many more VLPs are still in different stages of design, production, and approval. There are still many challenges that require to be addressed in the future before this surface display system can be widely used as an effective vaccine strategy in combating infectious diseases. In this review, we highlight the use of structural viral proteins to produce VLPs, emphasising their intrinsic properties, structural classification, and main expression host systems. We also compiled the recent scientific literature about VLP-based vaccines to underline the recent advances in their application as a vaccine strategy for preventing and fighting virulent human pathogens. Finally, we presented the key challenges and possible solutions for VLP-based vaccine production.

Time-restricted feeding regulates lipid metabolism under metabolic challenges.

Dysregulation of lipid metabolism is a commonly observed feature associated with metabolic syndrome and leads to the development of negative health outcomes such as obesity, diabetes mellitus, non-alcoholic fatty liver disease, or atherosclerosis. Time-restricted feeding/eating (TRF/TRE), an emerging dietary intervention, has been shown to promote pleiotropic health benefits including the alteration of diurnal expression of genes associated with lipid metabolism, as well as levels of lipid species. Although TRF likely induces a response in multiple organs leading to the modulation of lipid metabolism, a majority of the studies related to TRF effects on lipids have focused only on individual tissues, and furthermore there is a lack of insight into potential underlying mechanisms. In this review, we summarize the current insights regarding TRF effects on lipid metabolism and the potential mechanisms in adipose tissue, liver, skeletal muscle, and heart, and conclude by outlining possible avenues for future exploration.

An Introduction to Analytical Challenges, Approaches, and Applications in Mass Spectrometry-Based Secretomics.

The active release of proteins into the extracellular space and the proteolytic cleavage of cell surface proteins are key processes that coordinate and fine-tune a multitude of physiological functions. The entirety of proteins that fulfill these extracellular tasks are referred to as the secretome and are of special interest for the investigation of biomarkers of disease states and physiological processes related to cell-cell communication. LC-MS-based proteomics approaches are a valuable tool for the comprehensive and unbiased characterization of this important subproteome. This review discusses procedures, opportunities, and limitations of mass spectrometry-based secretomics to better understand and navigate the complex analytical landscape for studying protein secretion in biomedical science.

Metabolism and the Epigenome: A Dynamic Relationship.

Many chromatin-modifying enzymes require metabolic cofactors to support their catalytic activities, providing a direct path for fluctuations in metabolite availability to regulate the epigenome. Over the past decade, our knowledge of this link has grown significantly. What began with studies showing that cofactor availability drives global abundances of chromatin modifications has transitioned to discoveries highlighting metabolic enzymes as loci-specific regulators of gene expression. Here, we cover our current understanding of mechanisms that facilitate the dynamic and complex relationship between metabolism and the epigenome, focusing on the roles of essential metabolic and chromatin associated enzymes. We discuss physiological conditions where availability of these epimetabolites is dynamically altered, highlighting known links to the epigenome and proposing other plausible connections.

Natural killer cell-related anti-tumour adoptive cell immunotherapy.

Chimeric antigen receptor- (CAR-)modified T cells have been successfully used to treat blood cancer. With the improved research on anti-tumour adoptive cell therapy, researchers have focused on immune cells other than T lymphocytes. Natural killer (NK) cells have received widespread attention as barriers to natural immunity. Compared to T lymphocyte-related adoptive cell therapy, the use of NK cells to treat tumours does not cause graft-versus-host disease, significantly improving immunity. Moreover, NK cells have more sources than T cells, and the related modified cells are less expensive. NK cells function through several pathways in anti-tumour mechanisms. Currently, many anti-tumour clinical trials have used NK cell-related adoptive cell therapies. In this review, we have summarized the recent progress in NK cell-related adoptive cellular immunotherapy for tumour treatment and propose the current challenges faced by CAR-NK cell therapy.

Adipose tissue browning and thermogenesis under physiologically energetic challenges: a remodelled thermogenic system.

Metabolic diseases such as obesity and diabetes are often thought to be caused by reduced energy expenditure, which poses a serious threat to human health. Cold exposure, exercise and caloric restriction have been shown to promote adipose tissue browning and thermogenesis. These physiological interventions increase energy expenditure and thus have emerged as promising strategies for mitigating metabolic disorders. However, that increased adipose tissue browning and thermogenesis elevate thermogenic consumption is not a reasonable explanation when humans and animals confront energetic challenges imposed by these interventions. In this review, we collected numerous results on adipose tissue browning and whitening and evaluated this bi-directional conversion of adipocytes from the perspective of energy homeostasis. Here, we propose a new interpretation of the role of adipose tissue browning under energetic challenges: increased adipose tissue browning and thermogenesis under energy challenge is not to enhance energy expenditure, but to reestablish a more economical thermogenic pattern to maintain the core body temperature. This can be achieved by enhancing the contribution of non-shivering thermogenesis (adipose tissue browning and thermogenesis) and lowering shivering thermogenesis and high intensity shivering. Consequently, the proportion of heat production in fat increases and that in skeletal muscle decreases, enabling skeletal muscle to devote more energy reserves to overcoming environmental stress.

A review of the epidemiology of invasive fungal infections in Asian patients with hematological malignancies (2011-2021).

The incidence of invasive fungal infection (IFI) is increasing, especially among patients diagnosed with hematological malignancies due to their immunocompromised nature. Other risk factors include advanced age, exposure to immunosuppressants, neutropenia, and catheter use. Some of the most common IFI organisms reported are Candida and Aspergillus species, and other fungal species, including Scedosporium, Trichosporon, Cryptococcus, and Fusarium have also increasingly been reported in the past years. However, the epidemiologic data on IFI among patients with hematological malignancies in Asian countries are lacking. Therefore, we investigated published epidemiologic data on such cases from the past 10 years (2011-2021) and discuss the challenges faced in the diagnosis and management of IFIs in Asia.

Recruitment of informal caregivers into community oncology research studies: results from the 2022 Landscape Assessment.

Understanding the experiences of community oncology practices in recruiting informal (unpaid/family) caregivers into research studies can inform strategies to improve caregiver enrollment. We used data from the 2022 National Cancer Institute Community Oncology Research Program (NCORP) Landscape Assessment to describe the experience of recruiting informal caregivers for research studies in community oncology practices. Among 258 practice groups, only one-third (30%, 78/258) reported prior experience recruiting informal caregivers for research studies. In multivariable logistic analyses, having a greater number of oncology providers (increase per 10 providers, adjusted odds ratio [AOR] 1.16, 95% CI 1.03-1.31) and having advanced practice providers (APPs) involved in research (AOR 2.17, 95% CI 1.05-4.48) were significantly associated with prior experience recruiting caregivers. In conclusion, many community oncology practices lack caregiver recruitment experience and may benefit from education, integration of APPs/caregiver stakeholders in research infrastructure, and/or other strategies to improve caregiver recruitment.

FEAtl: a comprehensive web-based expression atlas for functional genomics in tropical and subtropical fruit crops.

BACKGROUND: Fruit crops, including tropical and subtropical fruits like Avocado (Persea americana), Fig (Ficus carica), Date Palm (Phoenix dactylifera), Mango (Mangifera indica), Guava (Psidium guajava), Papaya (Carica papaya), Pineapple (Ananas comosus), and Banana (Musa acuminata) are economically vital, contributing significantly to global agricultural output, as classified by the FAO's World Programme for the Census of Agriculture. Advancements in next-generation sequencing, have transformed fruit crop breeding by providing in-depth genomic and transcriptomic data. RNA sequencing enables high-throughput analysis of gene expression, and functional genomics, crucial for addressing horticultural challenges and enhancing fruit production. The genomic and expression data for key tropical and sub-tropical fruit crops is currently lacking a comprehensive expression atlas, revealing a significant gap in resources for horticulturists who require a unified platform with diverse datasets across various conditions and cultivars. RESULTS: The Fruit Expression Atlas (FEAtl), available at http://backlin.cabgrid.res.in/FEAtl/ , is a first-ever extensive and unified expression atlas for tropical and subtropical fruit crops developed using 3-tier architecture. The expressivity of coding and non-coding genes, encompassing 2,060 RNA-Seq samples across 91 tissue types and 177 BioProjects, it provides a comprehensive view of gene expression patterns for different tissues under various conditions. FEAtl features multiple tabs that cater to different aspects of the dataset, namely, Home, About, Analyze, Statistics, and Team and contains seven central functional modules: Transcript Information,Sample Information, Expression Profiles in FPKM and TPM, Functional Analysis, Genes Based on Tau Score, and Search for Specific Gene. The expression of a transcript of interest can be easily queried by searching by tissue ID and transcript type. Expression data can be displayed as a heat map, along with functional descriptions as well as Gene Ontology and Kyoto Encyclopedia of Genes and Genomes. CONCLUSIONS: This atlas represents a groundbreaking compilation of a wide array of information pertaining to eight distinct fruit crops and serves as a fundamental resource for comparative analysis among different fruit species and is a catalyst for functional genomic studies. Database availability: http://backlin.cabgrid.res.in/FEAtl/ .

Benefits and challenges experienced by participants on long-term methadone maintenance treatment in China: a qualitative study.

BACKGROUND: Methadone maintenance treatment (MMT) has been implemented in China for nearly two decades, with a significant decrease in the number of participants in recent years. However, there is a lack of comprehensive research focusing on the long-term effectiveness in the context of this decline, especially from the perspectives of MMT participants themselves. This study aims to address this gap by examining the benefits and challenges experienced by long-term MMT participants in China, to uncover potential causes of the decrease in participant numbers and to improve the effectiveness of the program. METHODS: We conducted semi-structured interviews with 21 long-term MMT participants (treatment duration ≥ 5 years) recruited through purposive sampling from 6 MMT clinics in the Guangdong Province, China, between December 2021 and August 2022. Thematic analysis was employed to analyze the transcribed interviews. Two analysts independently coded the data, and a third researcher double-coded 20% of transcripts to ensure intercoder reliability. RESULTS: Overall, participants corroborated the notable decline in MMT participants during their long-term MMT, citing death, arrest, and self-perceived abstinence from heroin, as their perceived driving factors. They reported positive changes in their health, family relationships, and social functioning. However, they identified economic hardship as their greatest challenge associated with MMT, further exacerbated by other barriers including the conflict of clinic opening hours and working schedules, discrimination from employers, and COVID-19-related restrictions. Additionally, participants identified issues with dose adjustment and emergency treatment continuation. CONCLUSIONS: This study outlines the overall improvement in the quality of life of long-term MMT participants. However, it highlights the need for official guidelines for dose adjustment and emergency treatment continuation as well as the provision of health education, job referrals, and flexibility of clinic opening times to facilitate the return to society receiving participants. Establishing a follow-up mechanism for those receiving MMT is also recommended to prevent relapses to heroin and other illicit substances.

Robust Covalent Organic Frameworks for Photosynthesis of H2O2: Advancements, Challenges and Strategies.

Since 2020, covalent organic frameworks (COFs) are emerging as robust catalysts for the photosynthesis of hydrogen peroxide (H2O2), benefiting from their distinct advantages. However, the current efficiency of H2O2 production and solar-to-chemical energy conversion efficiency (SCC) remain suboptimal due to various constraints in the reaction mechanism. Therefore, there is an imperative to propose efficiency improvement strategies to accelerate the development of this reaction system. This comprehensive review delineates recent advances, challenges, and strategies in utilizing COFs for photocatalytic H2O2 production. It explores the fundamentals and challenges (e.g., oxygen (O2) mass transfer rate, O2 adsorption capacity, response to sunlight, electron-hole separation efficiency, charge transfer efficiency, selectivity, and H2O2 desorption) associated with this process, as well as the advantages, applications, classification, and preparation strategies of COFs for this purpose. Various strategies to enhance the performance of COFs in H2O2 production are highlighted. The review aims to stimulate further advancements in utilizing COFs for photocatalytic H2O2 production and discusses potential prospects, challenges, and application areas in this field.

Electrocatalytic Synthesis of Urea: An In-depth Investigation from Material Modification to Mechanism Analysis.

Industrial urea synthesis production uses NH3 from the Haber-Bosch method, followed by the reaction of NH3 with CO2, which is an energy-consuming technique. More thorough evaluations of the electrocatalytic C-N coupling reaction are needed for the urea synthesis development process, catalyst design, and the underlying reaction mechanisms. However, challenges of adsorption and activation of reactant and suppression of side reactions still hinder its development, making the systematic review necessary. This review meticulously outlines the progress in electrochemical urea synthesis by utilizing different nitrogen (NO3 -, N2, NO2 -, and N2O) and carbon (CO2 and CO) sources. Additionally, it delves into advanced methods in materials design, such as doping, facet engineering, alloying, and vacancy introduction. Furthermore, the existing classes of urea synthesis catalysts are clearly defined, which include 2D nanomaterials, materials with Mott-Schottky structure, materials with artificially frustrated Lewis pairs, single-atom catalysts (SACs), and heteronuclear dual-atom catalysts (HDACs). A comprehensive analysis of the benefits, drawbacks, and latest developments in modern urea detection techniques is discussed. It is aspired that this review will serve as a valuable reference for subsequent designs of highly efficient electrocatalysts and the development of strategies to enhance the performance of electrochemical urea synthesis.

Rechargeable Zinc-Air Batteries: Advances, Challenges, and Prospects.

Rechargeable zinc-air batteries (Re-ZABs) are one of the most promising next-generation batteries that can hold more energy while being cost-effective and safer than existing devices. Nevertheless, zinc dendrites, non-portability, and limited charge-discharge cycles have long been obstacles to the commercialization of Re-ZABs. Over the past 30 years, milestone breakthroughs have been made in technical indicators (safety, high energy density, and long battery life), battery components (air cathode, zinc anode, and gas diffusion layer), and battery configurations (flexibility and portability), however, a comprehensive review on advanced design strategies for Re-ZABs system from multiple angles is still lacking. This review underscores the progress and strategies proposed so far to pursuit the high-efficiency Re-ZABs system, including the aspects of rechargeability (from primary to rechargeable), air cathode (from unifunctional to bifunctional), zinc anode (from dendritic to stable), electrolytes (from aqueous to non-aqueous), battery configurations (from non-portable to portable), and industrialization progress (from laboratorial to practical). Critical appraisals of the advanced modification approaches (such as surface/interface modulation, nanoconfinement catalysis, defect electrochemistry, synergistic electrocatalysis, etc.) are highlighted for cost-effective flexible Re-ZABs with good sustainability and high energy density. Finally, insights are further rendered properly for the future research directions of advanced zinc-air batteries.

Reproducibility of arterial spin labeling cerebral blood flow image processing: A report of the ISMRM open science initiative for perfusion imaging (OSIPI) and the ASL MRI challenge.

PURPOSE: Arterial spin labeling (ASL) is a widely used contrast-free MRI method for assessing cerebral blood flow (CBF). Despite the generally adopted ASL acquisition guidelines, there is still wide variability in ASL analysis. We explored this variability through the ISMRM-OSIPI ASL-MRI Challenge, aiming to establish best practices for more reproducible ASL analysis. METHODS: Eight teams analyzed the challenge data, which included a high-resolution T1-weighted anatomical image and 10 pseudo-continuous ASL datasets simulated using a digital reference object to generate ground-truth CBF values in normal and pathological states. We compared the accuracy of CBF quantification from each team's analysis to the ground truth across all voxels and within predefined brain regions. Reproducibility of CBF across analysis pipelines was assessed using the intra-class correlation coefficient (ICC), limits of agreement (LOA), and replicability of generating similar CBF estimates from different processing approaches. RESULTS: Absolute errors in CBF estimates compared to ground-truth synthetic data ranged from 18.36 to 48.12 mL/100 g/min. Realistic motion incorporated into three datasets produced the largest absolute error and variability between teams, with the least agreement (ICC and LOA) with ground-truth results. Fifty percent of the submissions were replicated, and one produced three times larger CBF errors (46.59 mL/100 g/min) compared to submitted results. CONCLUSIONS: Variability in CBF measurements, influenced by differences in image processing, especially to compensate for motion, highlights the significance of standardizing ASL analysis workflows. We provide a recommendation for ASL processing based on top-performing approaches as a step toward ASL standardization.

Addressing low-density malaria infections in India and other endemic part of the world-the opportune time?

Shifting from high- to low-malaria transmission accompanies a higher proportion of asymptomatic low-density malaria infections (LDMI). Currently, several endemic countries, such as India, are experiencing this shift as it is striving to eliminate malaria. LDMI is a complex concept for which there are several important questions yet unanswered on its natural history, infectiousness, epidemiology, and pathological and clinical impact. India is on the right path to eliminating malaria, but it is facing the LDMI problem. A brief discussion on the concept and definitions of LDMI is beforehand presented. Also, an exhaustive review and critical analysis of the existing literature on LDMI in malaria-endemic areas, including India, are included in this review. Finally, we opine that addressing LDMI in India is ethically and pragmatically achievable, and a pool of sine qua non conditions is required to efficiently and sustainably eliminate malaria.

Bacteriophage therapy against ESKAPE bacterial pathogens: Current status, strategies, challenges, and future scope.

The ESKAPE pathogens are the primary threat due to their constant spread of drug resistance worldwide. These pathogens are also regarded as opportunistic pathogens and could potentially cause nosocomial infections. Most of the ESKAPE pathogens have developed resistance to almost all the antibiotics that are used against them. Therefore, to deal with antimicrobial resistance, there is an urgent requirement for alternative non-antibiotic strategies to combat this rising issue of drug-resistant organisms. One of the promising alternatives to this scenario is implementing bacteriophage therapy. This under-explored mode of treatment in modern medicine has posed several concerns, such as preferable phages for the treatment, impact on the microbiome (or gut microflora), dose optimisation, safety, etc. The review will cover a rationale for phage therapy, clinical challenges, and propose phage therapy as an effective therapeutic against bacterial coinfections during pandemics. This review also addresses the expected uncertainties for administering the phage as a treatment against the ESKAPE pathogens and the advantages of using lytic phage over temperate, the immune response to phages, and phages in combinational therapies. The interaction between bacteria and bacteriophages in humans and countless animal models can also be used to design novel and futuristic therapeutics like personalised medicine or bacteriophages as anti-biofilm agents. Hence, this review explores different aspects of phage therapy and its potential to emerge as a frontline therapy against the ESKAPE bacterial pathogen.